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Issue - 134

Worthy Read

A step-by-step guide to analyzing data with Python and the Jupyter Notebook. This textbook will guide you through an investigation of money in politics using data from the California Civic Data Coalition. The course will teach you how to use pandas to read, filter, join, group, aggregate and rank structured data.

This post covers some higher-level software engineering principles demonstrated in my experience with Python testing over the past year and half. In particular, I want to revisit the idea of patching mock objects in unit tests.

As a DevOps leader it’s up to you to balance the autonomy and flexibility of a DevOps approach with the business value it was meant to create by making all your pipeline tools more collaborative, integrated, and automated. But challenges arise when you have multiple instances of the same tool, different tools with overlapping functionality, no ability to collaborate across teams—all resulting in unknown bottlenecks and complicated or no reporting. Read this Gartner research note to learn how to patch any leaks in your DevOps toolchain.

Speed and time is a key factor for any Data Scientist. In business, you do not usually work with toy datasets having thousands of samples. It is more likely that your datasets will contain millions or hundreds of millions samples. Customer orders, web logs, billing events, stock prices – datasets now are huge.

frontend development

A difficult decision for any Python team is whether to move from Python 2 and into Python 3. Although this is not a new decision for Python development teams, 2017 brings with it several important differences that make this decision crucial for proper forward planning. It feels like this is the year that we're really seeing the move to Python 3. It has been a long road, but Python 3 may finally have the upper hand.
Python 3


Tool for merging Conda (Anaconda) environment files into one file. This is used to merge your application environment file with any other environment file you might need (e.g. unit-tests, debugging, jupyter notebooks) and create a consistent environment without breaking dependencies from the previous environment files.
reader submission

Generate fake data using joke2k's faker and your own schema.
reader submission

Let’s dockerize a serious Django application. Curator's note - Love the humour in the article.

I’ve been itching to build my own cryptocurrency… and I shall give it an unoriginal name - Cranky Coin. After giving it a lot of thought, I decided to use Python. GIL thread concurrency is sufficient. Mining might suffer, but can be replaced with a C mining module. Most importantly, code will be easier to read for open source contributors and will be heavily unit tested. Using frozen pip dependencies, virtualenv, and vagrant or docker, we can fire this up fairly easily under any operating system.

This post will provide a step-by-step tutorial for creating and running a Jupyter widget.

In Less Than 50 Lines of Python.



yams - 57 Stars, 6 Fork
A collection of Ansible roles for automating infosec builds.

dependency - 16 Stars, 0 Fork
A dependency injection framework for Python.

ptime - 15 Stars, 1 Fork
IPython magic for parallel profiling.

sammy - 12 Stars, 1 Fork
Python library for generating AWS SAM (Serverless Application Model) templates with validation.

cpython_core_tutorial - 9 Stars, 0 Fork
Tutorial to contribute to the CPython project

bod - 3 Stars, 0 Fork
objdump beautifier

yacron: - 0 Stars, 0 Fork
A modern Cron replacement that is Docker-friendly.